Odtnhj

i have two Tensors, like this:

>>> xx_idx
<tf.Tensor 'Placeholder:0' shape=(100, ?) dtype=int64>
>>> xx_val
<tf.Tensor 'Placeholder_1:0' shape=(100, ?) dtype=float64>

How to create a SparseTensor from them? xx_idx are the indexes, xx_val are the values.
There are 100 samples.
The dimension of the vector is unknown, maybe 22000.

I tried this:

xx_vec = tf.SparseTensor(xx_idx, xx_val, 25000)

but here comes the error:

Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
NameError: name 'user_idx' is not defined
>>> xx_vec = tf.SparseTensor(xx_idx, xx_val, 25000)
Traceback (most recent call last):
 File "/home/work/tf/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py", line 671, in merge_with
 self.assert_same_rank(other)
 File "/home/work/tf/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py", line 716, in assert_same_rank
 other))
ValueError: Shapes (100, ?) and (?,) must have the same rank

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
 File "/home/work/tf/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py", line 746, in with_rank
 return self.merge_with(unknown_shape(ndims=rank))
 File "/home/work/tf/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py", line 677, in merge_with
 raise ValueError("Shapes %s and %s are not compatible" % (self, other))
ValueError: Shapes (100, ?) and (?,) are not compatible

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
 File "/home/work/tf/lib/python3.5/site-packages/tensorflow/python/framework/sparse_tensor.py", line 133, in __init__
 values_shape = values.get_shape().with_rank(1)
 File "/home/work/tf/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py", line 748, in with_rank
 raise ValueError("Shape %s must have rank %d" % (self, rank))
ValueError: Shape (100, ?) must have rank 1

problem solved

import tensorflow as tf
from tensorflow import TensorShape, Dimension


class GetVector:

 @classmethod
 def get_sparse_vector(cls, idx_all_0, val_all, dim_num):
 batch_size = idx_all_0.shape[0].value
 '''
 cur_idx = tf.placeholder(tf.int64, [batch_size, None, 1])
 cur_val = tf.placeholder(tf.float64, [batch_size, None])
 cur_vec = tf.placeholder(tf.float64, [batch_size, dim_num])
 '''
 idx_all = cls.idx_reform(idx_all_0)

 ans = 
 for i in range(batch_size):
 cur_idx = idx_all[i]
 cur_val = val_all[i]
 cur_vec = tf.SparseTensor(cur_idx, cur_val, TensorShape(Dimension(dim_num)))
 cur_vec_tensor = tf.sparse_tensor_to_dense(cur_vec)
 ans = tf.concat([ans, cur_vec_tensor], 0)
 ans = tf.reshape(ans, [batch_size, dim_num])
 return ans

 @classmethod
 def idx_reform(cls, idx_all):
 batch_size = idx_all.shape[0].value
 return tf.reshape(idx_all, [batch_size, -1, 1])